The present invention relates to traffic demand prediction, and more specifically, to uncertainty modeling in traffic demand prediction.
Traffic demand prediction helps to predict future travel demands of offering/taking a ride in a certain time period within a specific district, which enables ride-hailing service providers to maximize utilization of drivers willing to offer a ride and optimally satisfy passengers' needs of taking a vehicle.
Traditional travel demand prediction methods provided for predicting future travel demands mainly include four steps of trip generation, trip distribution, mode choice and trip assignment. These methods may be applied to travel demand prediction with coarser granularity and may provide future travel demands with low accuracy.
In recent years, new travel demand prediction methods are under study. For example, data-driven methods based on factorization machine or multi-output support vector regression machine may be used for travel demand prediction. With use of these methods, a deterministic prediction result of future travel demands would be acquired.